User Effort Detection

2. The system of claim 1, wherein the operations include adding valid sensed efforts to a cadence and habit model associated with a user.

3. The system of claim 1, wherein the operations include removing irregular and impostor sensed efforts from a cadence and habit model associated with a user.

4. The system of claim 1, wherein evaluating each sensed effort includes using a Mahalanobis algorithm.

5. The system of claim 1, wherein evaluating each sensed effort includes mapping each effort to an n-dimensional Euclidean space.

9. The system of claim 7, wherein evaluating the new sensed effort includes using a Mahalanobis algorithm.

11. The system of claim 10, wherein mapping is performed using one or more of Pearson product moment correlation coefficients, geospatial, ellipsoidal, and multidimensional effort metrics technologies, including Mahalanobis distance, Bhattacharyya distance, Cook's distance, Minkowski's distance, Random Sample Consensus, barycentricity, and outlier analysis statistical techniques.

12. The system of claim 10, wherein evaluating each sensed effort includes mapping each effort to an n-dimensional Euclidean space.

13. The system of claim 10, wherein mapping each sensed effort includes recursively evaluating bias and variability effects against dynamically created sets of reference standards that map against a single human effort metric collection contained in the collections.

14. The system of claim 10, wherein mapping each sensed effort includes identifying abnormalities that may not belong to the effort metrics produced by a specific person.

15. The system of claim 10, wherein the operations include using machine learning and artificial intelligence engines to develop independent rules about regular and anomalous human effort metrics specific to a single individual represented in the collections to derive a new human effort metrics collection from the combination of collections that corresponds to the measurements produced by a specific person, while still accounting for the unique expectations of variability for that person.

16. The system of claim 15, wherein the operations include identifying derived collections that represent mechanical attempts to create a set of human effort metrics to simulate a physical person.

18. The method of claim 17, wherein the method includes removing irregular and impostor sensed efforts from a cadence and habit model associated with a user.

19. The method of claim 17, wherein evaluating each sensed effort includes mapping each effort to an n-dimensional Euclidean space.